Technology of Graphic & Image
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1569-1572,1584

Improved pedestrian detection method based on depth residual network

Hao Xuzheng
Chai Zhengyi
School of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China

Abstract

To improve the accuracy of the pedestrian detection method, this paper proposed a rectangular input of convolution neural network enhance the new pedestrian detection method based on the depth residual network and YOLO object detection method. The rectangular input helped the model gain the pedestrian characteristics expression by analyzing the expression and distribution characteristics of pedestrians in the images. The depth residual network with pre-activation for YOLO object detection improved the feature extraction ability through more layers of convolution neural networks. Hybrid dataset training and cluster anchor boxes could also improve the pedestrian detection performance. The test results of INRIA dataset prove that the method has better detection performance than the popular pedestrian detection methods, the index of false positive per image can reduce to 13.86%, improving ranging from 1.51% to 58.62% in varying degrees.

Foundation Support

国家自然科学基金资助项目(U1504613)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0836
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 5
Section: Technology of Graphic & Image
Pages: 1569-1572,1584
Serial Number: 1001-3695(2019)05-060-1569-04

Publish History

[2019-05-05] Printed Article

Cite This Article

郝旭政, 柴争义. 一种改进的深度残差网络行人检测方法 [J]. 计算机应用研究, 2019, 36 (5): 1569-1572,1584. (Hao Xuzheng, Chai Zhengyi. Improved pedestrian detection method based on depth residual network [J]. Application Research of Computers, 2019, 36 (5): 1569-1572,1584. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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